def run(self):
"""Run the learner.
Returns a LearnerResult instance. Also sets self.result to that
instance.
"""
# max_time and max_iterations are mutually exclusive stopping critera
if 'max_time' not in self.options:
_stop = self._stop_after_iterations
else:
_stop = self._stop_after_time
self.stats = GreedyLearnerStatistics()
self.result = result.LearnerResult(self)
self.evaluator = evaluator.fromconfig(self.data, self.seed, self.prior)
self.evaluator.score_network(self.seed.copy())
first = True
self.result.start_run()
while not _stop():
self._run_without_restarts(_stop, self._restart,
randomize_net=(not first))
first = False
self.result.stop_run()
return self.result
def run(self):
self.result = result.LearnerResult(self)
self.evaluator = evaluator.fromconfig(self.data, prior_=self.prior)
self.result.start_run()
for net in self.networks:
self.result.add_network(net, self.evaluator.score_network(net))
self.result.stop_run()
return self.result
def run(self):
self.result = result.LearnerResult(self)
self.evaluator = evaluator.fromconfig(self.data, prior_=self.prior)
self.result.start_run()
for net in self.networks:
self.result.add_network(net, self.evaluator.score_network(net))
self.result.stop_run()
return self.result
def run(self):
self.result = result.LearnerResult(self)
self.evaluator = evaluator.fromconfig(self.data, prior_=self.prior)
self.result.start_run()
# algorithm here!
print 'Yay! Nothing to see here...'
# get prior!
# complete datasets!
# self.evaluator.score_network()
self.result.stop_run()
return self.result
def run(self): self.result = result.LearnerResult(self) self.evaluator = evaluator.fromconfig(self.data, prior_=self.prior) self.result.start_run() # algorithm here! print 'Yay! Nothing to see here...' # get prior! # complete datasets! # self.evaluator.score_network() self.result.stop_run() return self.result
def run(self):
"""Run the learner."""
self.stats = SALearnerStatistics(self.start_temp, self.delta_temp,
self.max_iters_at_temp)
self.result = result.LearnerResult(self)
self.evaluator = evaluator.fromconfig(self.data, self.seed, self.prior)
self.evaluator.score_network(self.seed.copy())
self.result.start_run()
curscore = self.evaluator.score_network()
# temperature decays exponentially, so we'll never get to 0.
# So, we continue until temp < 1
while self.stats.temp >= 1:
try:
newscore = self._alter_network_randomly_and_score()
except CannotAlterNetworkException:
return
self.result.add_network(self.evaluator.network, newscore)
if self._accept(newscore):
# set current score
self.stats.current_score = newscore
if self.stats.current_score > self.stats.best_score:
self.stats.best_score = self.stats.current_score
else:
# undo network alteration
self.evaluator.restore_network()
# temp not updated EVERY iteration. just whenever criteria met.
self.stats.update()
self.result.stop_run()
return self.result
